A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro

PA-824 inhibits porcine epidemic diarrhea virus infection in vivo and in vitro by inhibiting p53 activation

Porcine epidemic diarrhea virus (PEDV) is a type A coronavirus that causes severe watery diarrhea in piglets, resulting in severe economic losses worldwide. Therefore, new approaches to control PEDV infection are essential for a robust and sustainable pig industry. We screened 314 small-molecule drug libraries provided by Selleck and found that four drugs had obviously inhibitory effects on PEDV in Vero cells. PA-824, which had the highest SI index and the most reliable clinical safety, was selected for in vivo experiments. Animal attack tests showed that PA-824 effectively alleviated the clinical signs, intestinal pathological changes, and inflammatory responses in lactating piglets after PEDV infection. To further investigate the antiviral mechanism of PA-824, we measured the inhibitory effect of PA-824 on PEDV proliferation in a dose-dependent manner. By exploring the effect of PA-824 on the PEDV life cycle, we found that PA-824 acted directly on viral particles and hindered the adsorption, internalization, and replication phases of the virus, followed by molecular docking analysis to predict the interaction between PA-824 and PEDV non-structural proteins. Finally, we found that PA-824 could inhibit the apoptotic signaling pathway by suppressing PEDV-induced p53 activation. These results suggest that PA-824 could be protective against PEDV infection in piglets and could be developed as a drug or a feed additive to prevent and control PEDV diseases.IMPORTANCEPEDV is a highly contagious enteric coronavirus that widely spread worldwide, causing serious economic losses. There is no drug or vaccine to effectively control PEDV. In this study, we found that PA-824, a compound of mycobacteria causing pulmonary diseases, inhibited PEDV proliferation in both in vitro and in vivo. We also found that PA-824 directly acted on viral particles and hindered the adsorption, internalization, and replication stages of the virus. In addition, we found that PA-824 could inhibit the apoptotic signaling pathway by inhibiting PEDV-induced p53 activation. In conclusion, it is expected to be developed as a drug or a feed additive to prevent and control PEDV diseases.

Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification

DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 μM, 30.71 μM, and 74.84 μM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.

Structure-based discovery of potent myosin inhibitors to guide antiparasite drug development

Monogenea, a prevalent parasite in aquaculture, poses significant threats to the industry, leading to substantial losses. Current preventive measures have proven insufficient, necessitating the development of novel and effective anti-parasitic drugs. In this investigation, we obtained the full-length myosin cDNA sequence by analyzing three-generation transcriptome data, revealing a 5817-base sequence encoding 1938 amino acids. Subsequently, we modeled and analyzed the characteristics of the secondary and tertiary of myosin, pinpointing the crucial functional region within the motor domain (amino acids 1-768). The prokaryotic expression of this domain yielded a protein of 87.44 kDa, confirmed as myosin by Western Blotting. Molecular docking identified ASN439 as the key amino acid residue involved in arctigenin and myosin binding, a result corroborated by site-directed mutagenesis, affirming the active cavity of this interaction. Chalcone and shikonin were chosen from a virtual sieve of molecular library of natural drugs based on the active cavity. Chalcone and shikonin exhibited EC50 values of 1.085 mg/L and 0.371 mg/L, respectively, with corresponding IC50 values for myosin of 0.44 mM and 0.14 mM. Given its superior activity and structure, shikonin was selected for further optimization of drug molecule design, culminating in the discovery of 1,4-naphthoquinone as a potent antiparasitic agent. This compound demonstrated an EC50 of 0.047 mg/L, LC50 of 0.23 mg/L, and a TI index of 4.893. These findings collectively highlight the potential of shikonin and 1,4-naphthoquinone as alternative compounds to control Gyrodactylus infections. Further optimization of medicinal chemistry holds promise for the development of more potent 1,4-naphthoquinone analogues, offering prospects for future anthelmintic control through combinatorial or replacement strategies.

A review on in silico virtual screening methods in COVID-19 using anticancer drugs and other natural/chemical inhibitors

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Finding inhibitors against the druggable targets of SARS-CoV-2 has been a significant focus of research efforts across the globe. The primary motivation for using molecular modeling tools against SARS-CoV-2 was to identify candidates for use as therapeutic targets from a pharmacological database. In the published study, scientists used a combination of medication repurposing and virtual drug screening methodologies to target many structures of SARS-CoV-2. This virus plays an essential part in the maturation and replication of other viruses. In addition, the total binding free energy and molecular dynamics (MD) modeling findings showed that the dynamics of various medications and substances were stable; some of them have been tested experimentally against SARS-CoV-2. Different virtual screening (VS) methods have been discussed as potential means by which the evaluated medications that show strong binding to the active site might be repurposed for use against SARS-CoV-2.

In silico approaches and in vitro assays identify a coumarin derivative as antiviral potential against SARS-CoV-2

COVID-19, a disease caused by SARS-CoV-2, was declared a pandemic in 2020 and created a global crisis in health systems, with more than 545 million confirmed cases and 6.33 million deaths. In this sense, this work aims to identify possible inhibitors of the SARS-CoV-2 RdRp enzyme using in silico approaches. RdRp is a crucial enzyme in the replication and assembly cycle of new viral particles and a critical pharmacological target in the treatment of COVID-19. We performed a virtual screening based on molecular docking from our in-house chemical library, which contains a diversity of 313 structures from different chemical classes. Nine compounds were selected since they showed important interactions with the active site from RdRp. Next, the ADME-Tox in silico predictions served as a filter and selected the three most promising compounds: a coumarin LMed-052, a hydantoin LMed-087, and a guanidine LMed-250. Molecular dynamics simulations revealed details such as changes in the positions of ligands and catalytic residues during the simulations compared to the complex from molecular docking studies. Binding free energy analysis was performed using the MMGBSA method, demonstrating that LMed-052 and LMed-087 have better affinities for the RdRp by energetic contributions to the stability of the complexes when compared to LMed-250. Furthermore, LMed-052 showed significant in vitro inhibition against MHV-3, decreasing 99% of viral titers. Finally, these findings are useful to guide structural modifications aiming to improve the potential of these compounds to act as inhibitors of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.

Discovery of Potential Inhibitors of SARS-CoV-2 Main Protease by a Transfer Learning Method

The COVID-19 pandemic caused by SARS-CoV-2 remains a global public health threat and has prompted the development of antiviral therapies. Artificial intelligence may be one of the strategies to facilitate drug development for emerging and re-emerging diseases. The main protease (Mpro) of SARS-CoV-2 is an attractive drug target due to its essential role in the virus life cycle and high conservation among SARS-CoVs. In this study, we used a data augmentation method to boost transfer learning model performance in screening for potential inhibitors of SARS-CoV-2 Mpro. This method appeared to outperform graph convolution neural network, random forest and Chemprop on an external test set. The fine-tuned model was used to screen for a natural compound library and a de novo generated compound library. By combination with other in silico analysis methods, a total of 27 compounds were selected for experimental validation of anti-Mpro activities. Among all the selected hits, two compounds (gyssypol acetic acid and hyperoside) displayed inhibitory effects against Mpro with IC50 values of 67.6 μM and 235.8 μM, respectively. The results obtained in this study may suggest an effective strategy of discovering potential therapeutic leads for SARS-CoV-2 and other coronaviruses.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

The Influence of Mitochondrial Energy and 1C Metabolism on the Efficacy of Anticancer Drugs: Exploring Potential Mechanisms of Resistance

Mitochondria are the main energy factory in living cells. To rapidly proliferate and metastasize, neoplastic cells increase their energy requirements. Thus, mitochondria become one of the most important organelles for them. Indeed, much research shows the interplay between cancer chemoresistance and altered mitochondrial function. In this review, we focus on the differences in energy metabolism between cancer and normal cells to better understand their resistance and how to develop drugs targeting energy metabolism and nucleotide synthesis. One of the differences between cancer and normal cells is the higher nicotinamide adenine dinucleotide (NAD+) level, a cofactor for the tricarboxylic acid cycle (TCA), which enhances their proliferation and helps cancer cells survive under hypoxic conditions. An important change is a metabolic switch called the Warburg effect. This effect is based on the change of energy harvesting from oxygen-dependent transformation to oxidative phosphorylation (OXPHOS), adapting them to the tumor environment. Another mechanism is the high expression of one-carbon (1C) metabolism enzymes. Again, this allows cancer cells to increase proliferation by producing precursors for the synthesis of nucleotides and amino acids. We reviewed drugs in clinical practice and development targeting NAD+, OXPHOS, and 1C metabolism. Combining novel drugs with conventional antineoplastic agents may prove to be a promising new way of anticancer treatment.

Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor

The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi-coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma.

Elucidating host cell response pathways and repurposing therapeutics for SARS-CoV-2 and other coronaviruses

COVID-19, first reported in late 2019, is an ongoing pandemic that has been causing devastation across the globe. Although there are multiple vaccines that can prevent severe symptoms, effective COVID-19 therapeutics are still of importance. Using our proprietary in silico engine, we screened more than 22,000 unique compounds represented by over half a million gene expression profiles to uncover compounds that can be repurposed for SARS-CoV-2 and other coronaviruses in a timely and cost-efficient manner. We then tested 13 compounds in vitro and found three with potency against SARS-CoV-2 with reasonable cytotoxicity. Bortezomib and homoharringtonine are some of the most promising hits with IC₅₀ of 1.39 μM and 0.16 μM, respectively for SARS-CoV-2. Tanespimycin and homoharringtonine were effective against the common cold coronaviruses. In-depth analysis highlighted proteasome, ribosome, and heat shock pathways as key targets in modulating host responses during viral infection. Further studies of these pathways and compounds have provided novel and impactful insights into SARS-CoV-2 biology and host responses that could be further leveraged for COVID-19 therapeutics development.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

Z16b, a natural compound from Ganoderma cochlear is a novel RyR2 stabilizer preventing catecholaminergic polymorphic ventricular tachycardia

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited, lethal ventricular arrhythmia triggered by catecholamines. Mutations in genes that encode cardiac ryanodine receptor (RyR2) and proteins that regulate RyR2 activity cause enhanced diastolic Ca2+ release (leak) through the RyR2 channels, resulting in CPVT. Current therapies for CPVT are limited. We found that Z16b, a meroterpenoid isolated from Ganoderma cochlear, inhibited Ca2+ spark frequency (CaSF) in R2474S/ + cardiomyocytes in a dose-dependent manner, with an IC50 of 3.2 μM. Z16b also dose-dependently suppressed abnormal post-pacing Ca2+ release events. Intraperitoneal injection (i.p.) of epinephrine and caffeine stimulated sustained ventricular tachycardia in all R2474S/+ mice, while pretreatment with Z16b (0.5 mg/kg, i.p.) prevented ventricular arrhythmia in 9 of 10 mice, and Z16b administration immediately after the onset of VT abolished sVT in 9 of 12 mice. Of translational significance, Z16b significantly inhibited CaSF and abnormal Ca2+ release events in human CPVT iPS-CMs. Mechanistically, Z16b interacts with RyR2, enhancing the "zipping" state of the N-terminal and central domains of RyR2. A molecular docking simulation and point mutation and pulldown assays identified Z16b forms hydrogen bonds with Arg626, His1670, and Gln2126 in RyR2 as a triangle shape that anchors the NTD and CD interaction and thus stabilizes RyR2 in a tight "zipping" conformation. Our findings support that Z16b is a novel RyR2 stabilizer that can prevent CPVT. It may also serve as a lead compound with a new scaffold for the design of safer and more efficient drugs for treating CPVT.

Digging for the discovery of SARS-CoV-2 nsp12 inhibitors: a pharmacophore-based and molecular dynamics simulation study

Aim: COVID-19 is a global health threat. Therapeutics are urgently needed to cure patients severely infected with COVID-19. Objective: to investigate potential candidates of nsp12 inhibitors by searching for druggable cavity pockets within the viral protein and drug discovery. Methods: A virtual screening of ZINC natural products on SARS-CoV-2 nsp12's druggable cavity was performed. A lead compound with the highest affinity to nsp12 was simulated dynamically for 10 ns. Results: ZINC03977803 was nominated as the lead compound. The results showed stable interaction between ZINC03977803 and nsp12 during 10 ns. Discussion: ZINC03977803 showed stable interaction with the catalytic subunit of SARS-CoV-2, nsp12. It could inhibit the SARS-CoV-2 life cycle by direct interaction with nsp12 and inhibit RdRp complex formation.

DeepBindBC: a practical deep learning method for identifying native-like protein-ligand complexes in virtual screening

Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding. Our model incorporates information on non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. Here, we show that DeepBindBC outperforms Autodock Vina, Pafnucy, and DLSCORE for three DUD.E testing sets. Moreover, DeepBindBC identified a novel human pancreatic α-amylase binder validated by a fluorescence spectral experiment (K_a= 1.0×10⁵ M). Furthermore, DeepBindBC can be used as a core component of a hybrid virtual screening pipeline that incorporating many other complementary methods, such as DFCNN, Autodock Vina docking, and pocket molecular dynamics simulation. Additionally, an online web server based on the model is available at http://cbblab.siat.ac.cn/DeepBindBC/index.php for the user's convenience. Our model and the web server provide alternative tools in the early steps of drug discovery by providing accurate identification of native-like PLCs.

An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity

Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound’s ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC₅₀ values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery

Computational methods with affordable computational resources are highly desirable for identifying active drug leads from millions of compounds. This requires a model that is both highly efficient and relatively accurate, which cannot be achieved by most of the current methods. In real virtual screening (VS) application scenarios, the desired method should perform much better in selecting active compounds by prediction than by random chance. Here, we systematically evaluate the performance of our previously developed DFCNN model in large-scale virtual screening, and the results show our method has approximately 22 times the success rate compared to the random chance on average with a score cutoff of 0.99. Of the 102 test cases, 10 cases have more than 98 times the success rate of a random guess. Interestingly, in three cases, the prediction success rate is 99 times that of a random guess by a score cutoff of 0.99. This indicates that in most situations after our extremely large-scale VS, the dataset can be reduced 20 to 100 times for the next step of virtual screening based on docking or MD simulation. Furthermore, we have employed an experimental method to verify our computational method by finding several activity inhibitors for Trypsin I Protease. In addition, we also show its proof-of-concept application in de novo drug screening. The results indicate the massive potential of this method in the first step of the real drug development workflow. Moreover, DFCNN only takes about 0.0000225s for one protein–compound prediction on average with 80 Intel CPU cores (2.00 GHz) and 60 GB RAM, which is at least tens of thousands of times faster than AutoDock Vina or Schrödinger high-throughput virtual screening. Additionally, an online webserver based on DFCNN for large-scale screening is available at http://cbblab.siat.ac.cn/DFCNN/index.php for the convenience of the users.

Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease

The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple approaches to identify an effective approved drug (drug repurposing) that treats viral infections. Most of the drug repurposing approaches target spike protein or main protease. Here we use transmembrane serine protease 2 (TMPRSS2) as a target that can prevent the virus entry into the cell by interacting with the surface receptors. By hypothesizing that the TMPRSS2 binders may help prevent the virus entry into the cell, we performed a systematic drug screening over the current approved drug database. Furthermore, we screened the Enamine REAL fragments dataset against the TMPRSS2 and presented nine potential drug-like compounds that give us clues about which kinds of groups the pocket prefers to bind, aiding future structure-based drug design for COVID-19. Also, we employ molecular dynamics simulations, binding free energy calculations, and well-tempered metadynamics to validate the obtained candidate drug and fragment list. Our results suggested three potential FDA-approved drugs against human TMPRSS2 as a target. These findings may pave the way for more drugs to be exposed to TMPRSS2, and testing the efficacy of these drugs with biochemical experiments will help improve COVID-19 treatment.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

Repositioning anticancer drugs as novel COVID-19 antivirals: targeting structural and functional similarities between viral proteins and cancer

The current COVID-19 pandemic contributed by the SARS-CoV-2 has put in place an urgent need for new and promising antiviral therapeutics. The viral RNA-dependent RNA polymerase (RdRp) enzyme plays a vital role in viral replication for all RNA viruses, including SARS-CoV-2, thereby making it a prime and promising candidate for novel antiviral targeting. Interestingly, the human telomerase reverse transcriptase (hTERT), a common catalytic subunit of the telomerase enzyme in many cancers, has also been identified with structural and functional similarities to the viral RdRp. Therefore, it becomes essential to evaluate and consider anticancer drugs that target hTERT towards antiviral RdRp activity, and vice versa. For instance, Floxuridine, an hTERT inhibitor, and VX-222, a hepatitis C virus RdRp inhibitor, are now gaining recognition as a potential antiviral against SARS-CoV-2 and anti-hTERT for cancer, simultaneously. While limited studies on hTERT inhibitors for use as viral RdRp, and anti-RdRp inhibitors as hTERT inhibitors are available, in this review, we aim at bringing to light this close structural and functional relationship between both these enzymes. We punctuate this idea with specific examples on how potential anticancer inhibitors can effectively be brought to use as inhibitors against the SARS-CoV-2 virus, a relatively new pathogen, compared to the very well-studied field of cancer research.

Accelerating the discovery of the beyond rule of five compounds that have high affinities toward SARS-CoV-2 spike RBD

The battle against SARS-CoV-2 coronavirus is the focal point for the global pandemic that has affected millions of lives worldwide. The need for effective and selective therapeutics for the treatment of the disease caused by SARS-CoV-2 is critical. Herein, we performed a hierarchical computational approach incorporating molecular docking studies, molecular dynamics simulations, absolute binding energy calculations, and steered molecular dynamics simulations for the discovery of potential compounds with high affinity towards SARS-CoV-2 spike RBD. By leveraging ZINC15 database, a total of 1282 in-clinical and FDA approved drugs were filtered out from nearly 0.5 million protomers of relatively large compounds (MW > 500, and LogP ≤ 5). Our results depict plausible mechanistic aspects related to the blockage of SARS-CoV-2 spike RBD by the top hits discovered. We found that the most promising candidates, namely, ZINC95628821, ZINC95617623, ZINC3979524, and ZINC261494658, strongly bind to the spike RBD and interfere with the human ACE2 receptor. These findings accelerate the rational design of selective inhibitors targeting the spike RBD protein of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2

The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

A survey of circular RNAs in complex diseases: databases, tools and computational methods

Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.

Potent inhibition of Severe Acute Respiratory Syndrome Coronavirus 2 by photosensitizers compounds

The ongoing pandemic of coronavirus disease 2019 (COVID-19) posed a major challenge to the public health. Currently, no proven antiviral treatment for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is available. Here we report compounds pentalysine β-carbonylphthalocyanine zinc (ZnPc5K) and chlorin e6 (ce6) potently inhibited the viral infection and replication in vitro with EC₅₀ values at nanomolar level. These compounds were first identified by screening a panel of photosensitizers for photodynamic viral inactivation. Such viral inactivation strategy is implementable, and has unique advantages, including resistance to virus mutations, affordability compared to the monoclonal antibodies, and lack of long-term toxicity.

Integrated Multi-omics, Virtual Screening and Molecular Docking Analysis of Methicillin-Resistant Staphylococcus aureus USA300 for the Identification of Potential Therapeutic Targets: An In-Silico Approach

Staphylococcus aureus infection is a leading cause of mortality and morbidity in community, hospital and live-stock sectors, especially with the widespread emergence of methicillin-resistant S. aureus (MRSA) strains. To identify new drug molecules to treat MRSA patients, we have undertaken to search essential proteins that are indispensable for their survival but non-homologous to human host proteins. The current study utilizes a subtractive genome and proteome approach to screen the possible therapeutic targets against S. aureus USA300. Bacterial essential genes are obtained from the DEG database and are compared to avoid cross-reactivity with human host genes. In silico analysis shows 198 proteins that may be considered as therapeutic candidates. Depending on their sub-cellular localization, proteins are grouped as either vaccine or drug targets or both. Extracellular proteins such as cell division proteins (Q2FZ91, Q2FZ95), penicillin-binding proteins (Q2FZ94, Q2FYI0) of the bacterial cell wall, phosphoglucomutase (Q2FE11) and lipoteichoic acid synthase (Q2FIS2) are considered as vaccine targets, and their epitopes have been mapped. Altogether, 53 drug targets are identified, which have shown similarity with the drug targets available in the DrugBank database. Predicted drug targets belong to the common metabolic pathways of MRSA, such as fatty acid biosynthesis, folate biosynthesis, peptidoglycan biosynthesis, ribosome, etc. Protein-protein interaction analysis emphasizing peptidoglycan biosynthesis reveals the connection between penicillin-binding proteins, mur-family proteins and FemXAB proteins. In this study, staphylococcal FemA protein (P0A0A5) is subjected to structure-based virtual screening for the drug repurposing approach. There are 20 residues missing in the crystal structure of FemA, and 12 of these residues are located at the catalytic site. The missing residues are modelled, and stereochemistry is checked. FDA approved drugs available in the DrugBank database have been used in virtual screening with FemA in search of potential repurposed molecules. This approach provides us with 10 drugs that may be used in the treatment of methicillin-resistant staphylococcal mediated diseases. AutoDock 4.2 is used for in silico screening and shows a comparable inhibition constant (Ki) for all 10 FDA-approved drugs towards FemA. Most of these drugs are used in the treatment of various cancers, migraines and leukaemia. Protein-drug interaction analysis shows that the drugs mostly interact with hydrophobic residues of FemA. Moreover, Tyr328 and Lys383 contribute largely to hydrogen bondings during interactions. All interacting amino acids that bind to the drugs are part of the active site cavity of FemA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10989-021-10287-9.

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

BACKGROUND

Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task.

METHODOLOGY

This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models.

RESULTS

In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.

CONCLUSIONS

The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

Antiviral Efficacy of Pralatrexate against SARS-CoV-2

Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Chemoinformatic Analysis of Psychotropic and Antihistaminic Drugs in the Light of Experimental Anti-SARS-CoV-2 Activities

Introduction

There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients.

Objective

Based upon clinical observations, we proposed that some psychotropic and antihistaminic drugs could protect psychiatric patients from SARS-CoV-2 infection. This observation is investigated in the light of experimental in vitro data on SARS-CoV-2.

Methods

SARS-CoV-2 high-throughput screening results are available at the NCATS COVID-19 portal. We investigated the in vitro anti-viral activity of many psychotropic and antihistaminic drugs using chemoinformatics approaches.

Results and Discussion

We analyze our clinical observations in the light of SARS-CoV-2 experimental screening results and propose that several cationic amphiphilic psychotropic and antihistaminic drugs could protect people from SARS-CoV-2 infection; some of these molecules have very limited adverse effects and could be used as prophylactic drugs. Other cationic amphiphilic drugs used in other disease areas are also highlighted. Recent analyses of patient electronic health records reported by several research groups indicate that some of these molecules could be of interest at different stages of the disease progression. In addition, recently reported drug combination studies further suggest that it might be valuable to associate several cationic amphiphilic drugs. Taken together, these observations underline the need for clinical trials to fully evaluate the potentials of these molecules, some fitting in the so-called category of broad-spectrum antiviral agents. Repositioning orally available drugs that have moderate side effects and should act on molecular mechanisms less prone to drug resistance would indeed be of utmost importance to deal with COVID-19.